More in Cloud Platforms — page 8
Data Swamp: When a Data Lake Becomes Unusable
A data swamp is a data lake turned digital landfill, so disorganized that finding useful information is nearly impossible. This happens when data is dumped without metadata or quality checks, making it a costly, insecure liability instead of a valuable asset.
Cloud Alerting: Your System's Automated Smoke Detector
Cloud alerting is your system's smoke detector, watching key metrics and screaming when something's wrong before it becomes a fire. It's used to flag high CPU, failing health checks, or security anomalies.
Cloud Monitoring: Metrics, Time Series, and Resources
Cloud monitoring metrics are numerical measurements of a resource over time. They are used to build dashboards, trigger alerts when a threshold is crossed, and analyze performance for services like VMs or databases.
Automate Storage Costs with Lifecycle Policies
Object storage lifecycle policies are automated rules that move or delete data as it ages to save money. They're ideal for logs or backups, transitioning them to cheaper "cold" storage over time.
Cloud Governance: Rules for Your Cloud Kingdom
Cloud governance is like city planning for your cloud, setting automated rules to prevent chaos. It's used to control costs by blocking expensive VMs and enforce security with required settings.
The Ambassador Pattern: Your App's Diplomatic Sidecar
The Ambassador pattern places a proxy next to your application to handle its network communication, like a diplomat. This adds modern features like monitoring, security, and retries to legacy apps or across languages without changing app code.
The Sidecar Pattern: Offload and Isolate Application Logic
The Sidecar Pattern attaches a helper container to your main application, like a sidecar on a motorcycle. It offloads tasks like logging or proxying, letting you add features without changing the main app's code. The footgun is over-engineering a solution.
Circuit Breaker Pattern: Fail Fast, Not Hard
A circuit breaker wraps network calls to prevent cascading failures. It monitors for errors, and if a service seems down, it 'trips' to fail requests instantly without hitting the network. This gives the failing service time to recover.
What is Cloud Native Architecture?
Cloud Native refers to a collection of practices and open-source projects, like Kubernetes, governed by the CNCF. It's used by enterprises to build modern products and services.
Cloud Landing Zone: A Blueprint for Cloud Environments
A Cloud Landing Zone is a pre-configured, secure foundation for your cloud applications, like a city grid with utilities ready for new buildings. It provides shared services like networking and identity, ensuring consistency for large organizations.
Cloud Center of Excellence (CCoE): Your Internal Cloud Guides
A Cloud Center of Excellence (CCoE) is your company's internal cloud consulting team, centralizing expertise to guide adoption. It creates reusable patterns, manages costs, and ensures security, helping large orgs scale.
Cloud Business Case: Justifying Your Move to the Cloud
A cloud business case translates technical goals into financial terms like ROI and TCO to win executive support. It's the "why" and "how much" for a migration, used to secure budget and sponsorship.
AWS Well-Architected Framework: A Blueprint for Cloud Health
Think of it as a pre-flight checklist for your cloud architecture. It provides a consistent way to evaluate your systems against six pillars—like security and cost optimization—to ensure they are sound.
The 6 R's: Your Playbook for Cloud Migration
The 6 R's are a strategic menu for migrating apps to the cloud. When planning a move, you use it to decide whether to simply 'Rehost' an app, 'Refactor' it for performance, or even 'Retire' it.
Cloud Adoption Framework: Your Org's Cloud Migration Playbook
A Cloud Adoption Framework (CAF) is an organization's pre-flight checklist for moving to the cloud. It provides a structured path for strategy, planning, and governance, ensuring alignment during a large-scale migration.
Explainable AI (XAI): Why Did the Model Do That?
Explainable AI (XAI) translates a model's 'black box' decision into a human-readable reason. Use it to debug predictions, build user trust, or meet regulatory needs. The footgun: explanations are approximations of the model's logic, not absolute truth.
Model Drift: When Good Models Go Bad
A model is a snapshot of the world; model drift is the alarm that fires when the world changes but your snapshot has not. It detects when production data no longer statistically matches the training data, a common issue for models predicting user behavior.
Distributed Model Training: Splitting the Workload
Don't wait for one GPU to finish; use many. Distributed training splits a model's workload across multiple processors to finish faster. It's essential for massive deep learning models.
ML Inference Endpoint: The API for Your Model
An ML inference endpoint is the stable API URL your application calls to get predictions. It separates the public URL from the underlying model, letting you swap models without changing client code.
Hyperparameter Tuning for LLM Inference
Control an LLM's creativity versus predictability by tweaking its inference parameters. This is crucial for tasks like generating structured JSON versus creative text. The footgun is changing parameters without a clear goal, leading to chaotic output.